Computational modeling of drug disposition , Modeling techniques , Drug absorption , solubility , intestinal permeation , Drug distribution , Drug excretion , Active Transport , P-gp , BCRP , Nucleoside transporters , hPEPT1 , ASBT , OCT , OATP , BBB-choline transporter
Computational modelling of drug disposition lalitajoshi9
computational modelling of drug disposition is the integral part of computer aided drug design. different kinds of tools being used in the prediction of drug disposition in human body. This topic in the CADD explains the details about the drug disposition, active transporters and tools.
Computational modelling of drug disposition lalitajoshi9
computational modelling of drug disposition is the integral part of computer aided drug design. different kinds of tools being used in the prediction of drug disposition in human body. This topic in the CADD explains the details about the drug disposition, active transporters and tools.
REGULATORY AND INDUSTRY VIEWS ON QbD, SCIENTIFICALLY BASED QbD- EXAMPLES OF A...Ardra Krishna
The pharmaceutical Quantity by Design (QbD) is a systemic approach to development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quantity risk management.
QbD has been adopted by U.S Food and Drug Administration (FDA) for the discovery, development and manufacture of drugs.
Quality- by- design (QbD) is a concept introduces by the International Conference on Harmonization (ICH) Q8 guidelines.
Myself Omkar Tipugade , M- Pharm ,Sem - II, Department of pharmaceutics , from Shree Santkrupa College Of Pharmacy , ghogaon . Today I upload presentation on Active Transport like P-gp , BCPR, Nucleoside transporters etc .
LEGAL PROTECTION OF INNOVATIVE USES OF COMPUTERS IN R & D.pptxTanvi Mhashakhetri
CONTENTS :
Introduction
Intellectual Property Rights
Patents
Patents on Algorithms
Patents on Human Interfaces
Patents on Machine-Machine Interfaces
Patents on Data Structures
Copyright
Protection of Databases
Trade Secrets
Enforcement of Rights
Conclusion
References
INTRODUCTION :
The days in which IP (intellectual property) strategists were separated into groups of pharmacologists (chemists or biologists) and other groups of computer scientists are slowly passing—in the same manner in which the technologies are increasingly overlapping in the scientific world.
Pharmacology patent lawyers had typically spent their training in the laboratory working with chemicals or using polymerase chain reaction (PCR) techniques; they understood how small molecular entities functioned and characterized sequences of RNA, DNA, and proteins.
Computer scientists, on the other hand, spent hours programming computers and later writing software and business method patents.
Just as understanding the application of computers in pharmacology presents a challenge for researchers in both fields, it also means that the IP specialists also need to combine strategies from both fields to obtain the best possible legal protection for innovation.
A few years ago a study carried out by the London-based consulting firm Silico Research reported that very few patent applications had been filed in bioinformatics.
The reasons cited in the study for the scarcity of patents included the fact that many current bioinformatics products merely combined existing data sources into a single product and the difficulty of proving infringement of software patents.
The United States Patent and Trademark Office (USPTO) recognized in 1999 that bioinformatics represented a special challenge and that same year created a special examination group—Art Unit 1631—to examine the increasing number of applications .
Since these studies were published, however, the growth in the number of bioinformatics patents seems to have stalled.
INTELLECTUAL PROPERTY RIGHTS
The term “ Intellectual property Rights” is used to describe the legal instrument for protecting innovation .
There are intellectual property issues associated with four elements of a software program:
Program function - whether the algorithm is performed by the hardware or the software,
External design - the conventions for communication between the program and the user or other programs,
User interfaces - the interactions between the program and the user,
Program code - the implementation of the function and external design of the program.
CONCLUSION
The use of computers in developing new pharmaceutical products is nowadays common place, and a number of tools and databases have been developed to improve their use. Although intellectual property rights have to date rarely been the subject of court cases.
• In silico (literally alluding the mass use of silicon for semiconductor computer chips) is an expression used to performed on computer or via computer simulation
• In silico tools capable of identifying critical factors (i.e. drug physicochemical properties, dosage form factors) influencing drug in vivo performance, and predicting drug absorption based on the selected data set (s) of input factors.
REGULATORY AND INDUSTRY VIEWS ON QbD, SCIENTIFICALLY BASED QbD- EXAMPLES OF A...Ardra Krishna
The pharmaceutical Quantity by Design (QbD) is a systemic approach to development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quantity risk management.
QbD has been adopted by U.S Food and Drug Administration (FDA) for the discovery, development and manufacture of drugs.
Quality- by- design (QbD) is a concept introduces by the International Conference on Harmonization (ICH) Q8 guidelines.
Myself Omkar Tipugade , M- Pharm ,Sem - II, Department of pharmaceutics , from Shree Santkrupa College Of Pharmacy , ghogaon . Today I upload presentation on Active Transport like P-gp , BCPR, Nucleoside transporters etc .
LEGAL PROTECTION OF INNOVATIVE USES OF COMPUTERS IN R & D.pptxTanvi Mhashakhetri
CONTENTS :
Introduction
Intellectual Property Rights
Patents
Patents on Algorithms
Patents on Human Interfaces
Patents on Machine-Machine Interfaces
Patents on Data Structures
Copyright
Protection of Databases
Trade Secrets
Enforcement of Rights
Conclusion
References
INTRODUCTION :
The days in which IP (intellectual property) strategists were separated into groups of pharmacologists (chemists or biologists) and other groups of computer scientists are slowly passing—in the same manner in which the technologies are increasingly overlapping in the scientific world.
Pharmacology patent lawyers had typically spent their training in the laboratory working with chemicals or using polymerase chain reaction (PCR) techniques; they understood how small molecular entities functioned and characterized sequences of RNA, DNA, and proteins.
Computer scientists, on the other hand, spent hours programming computers and later writing software and business method patents.
Just as understanding the application of computers in pharmacology presents a challenge for researchers in both fields, it also means that the IP specialists also need to combine strategies from both fields to obtain the best possible legal protection for innovation.
A few years ago a study carried out by the London-based consulting firm Silico Research reported that very few patent applications had been filed in bioinformatics.
The reasons cited in the study for the scarcity of patents included the fact that many current bioinformatics products merely combined existing data sources into a single product and the difficulty of proving infringement of software patents.
The United States Patent and Trademark Office (USPTO) recognized in 1999 that bioinformatics represented a special challenge and that same year created a special examination group—Art Unit 1631—to examine the increasing number of applications .
Since these studies were published, however, the growth in the number of bioinformatics patents seems to have stalled.
INTELLECTUAL PROPERTY RIGHTS
The term “ Intellectual property Rights” is used to describe the legal instrument for protecting innovation .
There are intellectual property issues associated with four elements of a software program:
Program function - whether the algorithm is performed by the hardware or the software,
External design - the conventions for communication between the program and the user or other programs,
User interfaces - the interactions between the program and the user,
Program code - the implementation of the function and external design of the program.
CONCLUSION
The use of computers in developing new pharmaceutical products is nowadays common place, and a number of tools and databases have been developed to improve their use. Although intellectual property rights have to date rarely been the subject of court cases.
• In silico (literally alluding the mass use of silicon for semiconductor computer chips) is an expression used to performed on computer or via computer simulation
• In silico tools capable of identifying critical factors (i.e. drug physicochemical properties, dosage form factors) influencing drug in vivo performance, and predicting drug absorption based on the selected data set (s) of input factors.
computational modeling of drug disposition Naveen Reddy
Computational Modelling of Drug disposition, modelling techniques, drug absorption, drug distribution, drug Excretion, quantitative approach, qualitative approach, in silico models, blood brain barrier, plasma protein binding, QSAR, QSPR, Volume of distribution
COMPUTATIONAL MODELING IN DRUG DISPOSITION.pptxMohammad Azhar
Computational modeling is the use of computers to simulate and study complex systems using mathematics, physics, and computer science. It is a powerful tool that can be used to understand and predict how systems behave, without having to conduct physical experiments.
One way to think about computational modeling is to imagine a virtual world that you can create and control. You can use this virtual world to test different scenarios and see how the system behaves under different conditions.
For example, you could create a computational model of a weather system to predict how a hurricane is going to develop or, you could create a computational model of a drug to predict how it will interact with the human body.
Data Collection - Collecting experimental data on drug properties and interactions.
Model Development - Building mathematical models that represent drug behavior in the body.
Model Validation - Ensuring that models accurately predict real-world outcomes.
Model Application - Using models for various purposes like drug design, dose optimization, and clinical trial simulations.
COMPUTATIONAL MODELING OF DRUG DISPOSITION.pptxPoojaArya34
Computational modelling of drug disposition is the integral part of computer aided drug design. different kinds of tools being used in the prediction of drug disposition in human body. This topic in the CADD explains the details about the drug disposition, active transporters and tools.
Historically, drug discovery has focused almost exclusively on efficacy and selectivity against the biological target.
As a result, nearly half of drug candidates fail at phase II and phase III clinical trials because of undesirable drug pharmacokinetics properties, including absorption, distribution, metabolism, excretion, and toxicity (ADMET).
The pressure to control the escalating cost of new drug development has changed the paradigm since the mid-1990s. To reduce the attrition rate at more expensive later stages, in vitroevaluation of ADMET properties in the early phase of drug discovery has been widely adopted.Many high-throughput in vitro ADMET property screening assays have been developed and applied successfully .
For example, Caco-2 and MDCK cell monolayers are widely used to simulate membrane permeability as an in vitro estimation of in vivo absorption.
These in vitro results have enabled the training of in silico models, which could be applied to predict the ADMET properties of compounds even before they are synthesized.
Myself Omkar Tipugade , M - Pharm sem II , department of Pharmaceutics , today will upload presentation on Computational modeling in drug disposition .
Gastrointestinal absorption simulation using in silico methodology; by Dr. Bh...bhupenkalita7
This PPT includes a brief introduction of in silico models for simulation of GI absorption of drugs, principles involved in the dvelopment of computational models for in silico pharmacokinetic studies related to absorption of drugs from GI tract.
DRUG DISPOSITION COMPUTATIONAL MODELING.pptxManshiRana2
Drug development has traditionally focused entirely on efficacy and selectivity against the biological target.
As a result, roughly 50% of drug candidates fail in phase ii and phase iii clinical trials due to unfavorable pharmacokinetic features, such as absorption, distribution, metabolism, excretion, and toxicity (admet).
Since the mid-1990s, the pressure to control the rising cost of new medication development has shifted the paradigm.
Invitro evaluation of admet characteristics in the early phases of drug discovery has been widely adopted to avoid attrition at more expensive later stages.
NIOSOMES , GENERAL CHARACTERISTICS OF NIOSOME , TYPES OF NIOSOMES , OTHERS TYPES OF NIOSOMES , NIOSOMES VS LIPOSOMES , COMPONENTS OF NIOSOMES , Non-ionic surfactant , Cholesterol , Charge inducing molecule , METHOD OF PREPARATION , preparation of small unilamellar vesicles , Sonication , Micro fluidization , preparation of large unilamellar vesicles , Reverse Phase Evaporation , Ether Injection , preparation of Multilamellar vesicles , Hand shaking method , Trans membrane pH gradient drug uptake process (remote loading) , Miscellaneous method :Multiple membrane extrusion method , The “Bubble” Method , Formation of Niosomes From Proniosomes , SEPARATION OF UNENTRAPPED DRUGS , Gel Filtration , Dialysis , Centrifugation , FACTORS AFFECTING THE PHYSICOCHEMICAL PROPERTIES OF NIOSOMES , Membrane Additives , Temperature of Hydration , PROPERTIES OF DRUGS , AMOUNT AND TYPE OF SURFACTANT
Structure of Surfactants , Resistance to Osmotic Stress , Characterization of niosomes ,Therapeutic applications of Niosomes , For Controlled Release of Drugs , To Improve the Stability and Physical Properties of the Drugs , For Targeting and Retention of Drug in Blood Circulation , Proniosomes , Aspasomes , Vesicles in Water and Oil System (v/w/o) ,Bola - niosomes , Discomes , Deformable niosomes or elastic niosomes , According to the nature of lamellarity ,Small Unilamellar vesicles (SUV) 25 – 500 nm in size.,Large Unilamellar vesicles (LUV) 0.1 – 1μm in size , Multilamellar vesicles (MLV) 1-5 μm in size , According to the size:Small Niosomes (100 nm – 200 nm) , Large Niosomes (800 nm – 900 nm),Big Niosomes (2 μm – 4 μm)
Drug absorption from git , Drug absorption from git , DIGESTION AND ABSORPTION , Transcellular / intracellular , transport , .Passive Transport Processes , Passive diffusion , Pore transport , Ion- pair transport , Facilitated or mediated diffusion
, Active transport processes , Primary , Secondary , Symport (Co-transport) , Antiport (Counter transport) , Paracellular / Intercellular Transport , Permeation through tight junctions of epithelial cells , Persorption , Vesicular or Corpuscular Transport (Endocytosis) , Pinocytosis , Phagocytosis , FACTORS INFLUENCING ABSORPTION OF DRUGS , DRUG DISSOLUTION , Factors affecting dissolution rate , DISSOLUTION APPARATUS , IVIVC (In vitro- in vivo correlation) , ROLE OF DOSAGE FORM , Transport model , pH Microclimate , Intracellular pH environment , Tight junction complex
Cold cream , vanishing cream , IDEAL PROPERTIES OF VANISHING CREAMS , MAJOR INGREDIENTS USED FOR THE PRODUCTION OF VANISHING CREAMS , FORMULATION OF VANISHING CREAM , IDEAL CHARACTERISTICS OF COLD CREAM , INGREDIENTS USED FOR PREPARATION OF COLD CREAM , FORMULATION OF COLD CREAM
HERBAL INGREDIENTS USED IN HAIR CARE , cosmetics , herbal cosmetics , Herbal ingredients used in the cosmetics , preparation for hair , Hair oil , Shampoos , Hair dye , Hair lotion , Ingredient used in Herbal hair oil , Marketed herbal hair oil , Evaluation of herbal hair oil , Ingredient used in Herbal shampoo , Marketed herbal shampoo , Evaluation of herbal shampoo , Ingredient used in the herbal hair dye , Marketed herbal hair dye , Evaluation of herbal hair dye
cosmetics - regulatory : Regulatory provisions related to cosmetics PV. Viji
REGULATORY PROVISIONS RELATED TO COSMETICS , REGULATORY PROVISIONS RELATING TO IMPORT OF COSMETICS , Application for registration certification for import cosmetics , Grant of registration certificate , Standards for imported cosmetics , REGULATORY PROVISIONS RELATING TO MANUFACTURE OF COSMETICS , REQUIREMENTS OF FACTORY PREMISES FOR MANUFACTURE OF COSMETICS , LOAN LICENCE
INDIAN REGULATORY REQUIREMENTS FOR LABELING OF COSMETICSPV. Viji
INDIAN REGULATORY REQUIREMENTS FOR LABELING OF COSMETICS , IMPORTANCE OF LABELING , LABELING REQUIREMENTS , Common or generic name of the product. , Product function , Use instruction , Name & address of Manufacturer , Country of manufacture , Manufacture Date , Expiry date , Net Quantity , Retail Sale Price , Storage condition , Barcodes , Batch number , Warning or Caution if hazard exists , Manufacturing License Number , Ingredients , Registration Certificate Number (RCN) , Consumer Care Details , Using Stickers , Brown/Red or green dot , Not a standard pack size under Legal Metrology(Packaged commodities) Rules
Statistical modeling in pharmaceutical research and developmentPV. Viji
Statistical modeling in pharmaceutical research and development , Statistical Modeling , Descriptive Versus Mechanistic Modeling , Statistical Parameters Estimation , Confidence Regions , Non Linearity at the Optimum , Sensitivity Analysis , Optimal Design , Population Modeling
NMR SPECTROSCOPY ,Relaxation,longitudinal / spin- spin relaxation,transverse / spin- spin relaxation,Shielding of proton ,Deshielding of proton,CHEMICAL SHIFT,Factors Influencing Chemical Shift,Inductive effect, Vander Waal’s deshielding,Anisotropic effect (space effect),Hydrogen bonding
,SPLITTING OF THE SIGNALS,COUPLING CONSTANT,NMR SIGNAL IN VARIOUS COMPOUND
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
2. CONTENTS
• Introduction
• Modeling techniques
• Drug absorption
i. solubility
ii. intestinal permeation
iii. other considerations
• Drug distribution
• Drug excretion
2
3. • Active Transport
i. P-gp
ii. BCRP
iii. Nucleoside transporters
iv. hPEPT1
v. ASBT
vi. OCT
vii. OATP
viii. BBB-choline transporter
• References
3
4. INTRODUCTION
• Historically , drug discovery has focused almost exclusively on efficacy and selectivity against the biological
target.
• As a result, nearly half of drug candidates fail at phase 2 and phase 3 clinical trials because of undesirable
drug pharmacokinetics properties , including absorption , distribution, metabolism, excretion , and toxicity
(ADMET).
• The pressure to control the escalating cost of new drug development has changed the paradigm since the mid-
1990s.
• To reduce the attrition rate at more expensive later stages , in vitro evaluation od ADMET properties in the
early phase of drug discovery has been widely adopted.
4
5. • Many high-throughput and in vitro ADMET property screening assays have been developed and applied
successfully.
• For example: Caco-2 and MDCK ( Madin - Darby canine kidney Epithelial Cells ) monolayers are widely
used to stimulate membrane permeability as an in vitro estimation of in vivo absorption.
• These in vitro results have enabled the training of in silico models , which could be applied to predict the
ADMET properties of compounds even before they are synthesized.
• Fueled by the ever-increasing computational power and significant advances of in silico modeling algorithms ,
numerous computational programs that aim at modeling drug ADMET properties have emerged.
5
6. • A comprehensive list of available commercial ADMET modeling software has been provided previously
by van de Waterbeemd and Gifford.
• In these chapter focuses on in silico modeling of drug disposition including absorption , distribution ,
and excretion.
• This chapter concludes with the challenges and future trends of in silico drug disposition property
modeling.
6
9. MODELING TECHNIQUES
• Two types of modeling approaches are:
1. Quantitative approaches
2. Qualitative approaches.
9
10. 1. QUANTITATIVE APPROACHES
• The quantitative approaches represented by pharmacophore modeling and flexible docking studies
investigate the structural requirements for the interaction , between drugs and the targets that are involved in
ADMET processes.
• These are especially useful when there is an accumulation of knowledge against a certain target.
• For example , a set of drugs known to be transported by a transporter would enable a pharmacophore study to
elucidate the minimum required structural features for transport.
• The availability of a protein’s three-dimensional structure , from either X-ray crystallization or homology
modeling , would assist flexible docking of the active ligand to derive important interactions between the
protein and the ligand.
10
11. • Three widely used automated pharmacophore perception tools.
a) DISCO (DIStance Comparisons)
b) GASP (Genetic algorithm similarity program)
c) catalyst/HIPHOP
• All three programs attempt to determine common features based on the superposition of active
compounds with different algorithms.
• The application of different flexible docking algorithms in drug discovery has recently been reviewed.
• The essential interactions derived from either study can be used as a screen in evaluating drug
ADMET properties.
11
12. 2.QUALITATIVE APPROACHES
• It represented by quantitative structure-activity relationship (QSAR ) and quantitative structure-property
relationship (QSPR) Studies utilize multivariate analysis to correlate molecular descriptors with ADMET-
related properties.
• A diverse range of molecular descriptors can be calculated based on the drug structure.
• Some of these descriptors are closely related to a physical property and are easy to comprehend (e.g.,
molecular weight ),whereas the majority of the descriptors are of quantum mechanical concepts or
interaction energies at dispersed space points that are beyond simple physicochemical parameters.
12
13. • When calculating correlations , it is important to select the molecular descriptors that represent the type of
interactions contributed to the targeted biological property.
• A set of descriptors that specifically target ADME related properties has been proposed by Cruciani and
colleagues.
• The majority of published ADMET models are generated based on 2D descriptors.
• Even though the alignment-dependent 3D descriptors that are relevant to the targeted biological activity
tend to generate the most predictive models.
13
14. • The difficulties inherent in structure alignment thwart attempts to apply this type of modeling in a high-
throughput manner. This has prompted the development of alignment independent 3D descriptors.
However, most of these descriptors to date are still insufficiently discriminating.
• A wide selection of statistical algorithms is available to researchers for correlating field descriptors with
ADMET properties including
1. Simple multiple linear regression (MLR)
2. Multivariate partial least-squares (PLS)
14
15. • Nonlinear regression-type
i. Algorithms Artificial neural networks (ANN)
ii. Support vector machine (SVM)
• No one method can consistently perform better than the others. Just
like descriptor selection, it is essential to select the right
mathematical tool for most effective ADMET modeling. Sometimes
it is necessary to apply multiple statistical methods and compare the
results to identify the best approach, as illustrated in a recent
solubility QSPR model
15
18. DRUG ABSORPTION
• Because of its convenience and good patient compliance , oral administration is the most preferred drug
delivery form.
• As a result , much of the attention of in silicon approaches is focused on modeling drug oral absorption ,
which mainly occurs in the human intestine.
• In general drug bioavailability and absorption is the result of the interplay between drug solubility and
intestinal permeability.
18
19. SOLUBILITY
• A drug generally must dissolve before it can be absorbed from the intestinal lumen. Direct measurement of
solubility is time-consuming and requires a large amount of (expensive) compound at the milligram scale
• By measuring a drug’s log P value (log of the partition coefficient of the compound between water and n-
octanol)and its melting point , one could indirectly estimate solubility using the “general solubility
equation”.
• Even though the process is simplified, it still requires the synthesis of the compound.
• To predict the solubility of the compound even before synthesizing it , in silico modeling can be
implemented.
19
20. There are mainly two approaches to modeling solubility.
1. One is Based on the underlying physiological processes.
2. Other is an empirical approach.
• The dissolution process involves the breaking up of the solute from its crystal lattice and the association of the
solute with solvent molecules. Obviously, weaker interactions within the crystal lattice (lower melting point)
and stronger interactions between solute and solvent molecules will result in better solubility and vice versa. For
drug like molecules, solvent-solute interaction has been the major determinant of solubility and its prediction
attracts most efforts.
• Log P is the simplest estimation of solvent-solute interaction and can be readily predicted with commercial
programs such as CLogP ( Daylight Chemical Information systems , aliso Viejo ,CA) which utilizes a fragment
based approach.
20
21. • To recognize the contribution of solute crystal lattice energy in determining solubility, other approaches
amended LogP values with additional terms for more accurate predictions.
• Empirical approaches , represented by QSPR , utilize multivariate analyses to identify correlations between
molecular descriptors and solubility.
• Even though the calculation process ignores the underlying physiological processes, the molecular descriptor
selection and model interpretation still requires understanding of the dissolution process.
• Selection of field descriptors that adequately describe the physiological process and the appropriate
multivariate analysis is essential successful modeling.
21
22. INTESTINAL PERMEATION
• Intestinal permeation describes the ability of drugs to cross the intestinal mucosa separating the gut
lumen from the portal circulation.
• It is an essential process for drugs to pass the intestinal membrane before entering the systemic
circulation to reach their target site of action.
• The process involves both passive diffusion and active transport.
• It is a complex process that is difficult to predict solely based on molecular mechanism.
22
23. • As a result , most current models aim to simulate in vitro membrane permeation of Caco-2 ,
MDCK or PAMPA , which have been a useful indicator of in vivo drug absorption.
23
24. OTHER CONSIDERATIONS
• The ionization state will affect both solubility and permeability and , as a result , influence the absorption
profile of a compound.
• Given the environmental pH , the charge of a molecule can be determined using the compound’s ionization
constant value (pka) , which indicates the strength of an acid or a base.
• Several commercially and publicly available programs provide pka estimation based on the input structure ,
including
• SCSpka ( Chemsilico , Tewksbury , MA )
• Pallas/pKalc ( CompuDrug , Sedona , AZ)
• ACD / pKa (ACD ,Toronto , ON , Canada )
• SPARC Online calculator.
24
25. • Both influx and efflux transporters are located in intestinal epithelial cells and can either increase or
decrease oral absorption.
• Influx transporters such as human peptide transporter 1 (hPEPT1), apical sodium bile acid transporter
(ASBT ) , and nucleoside transporters actively transport drugs that mimic their native substrates across the
epithelial cell.
• Efflux transporters such as P-glycoprotein (P-gp) , multidrug resistance-associated protein (MRP) , and
Breast Cancer resistance protein (BCRP) Actively pump absorbed drugs back into the intestinal lumen.
• Commercial packages such as Gastro plus ( simulations plus , Lancaster , CA ) and iDEA ( Lion
Bioscience , Inc. Cambridge , MA) are available to predict oral absorption and other pharmacokinetic
properties.
• They are both based on the advanced compartmental absorption and transit (CAT) model [20], which
incorporates the effects of drug moving through the gastrointestinal tract and its absorption into each
compartment at the same time
25
27. DRUG DISTRIBUTION
• Distribution is an important aspect of a drug’s pharmacokinetic profile.
• The structural and physiochemical properties of a drug determine the extent of its distribution ,
which is mainly reflected by three parameters:
1. Volume of distribution (VD)
2. Plasma- protein binding ( PPB)
3. Blood-brain barrier (BBB) Permeability
27
28. 1.VOLUME OF DISTRIBUTION (VD)
• Vd is a measure of relative partitioning of drug between plasma and tissue, an important
proportional constant that, when combined a drug is a major determinant of how often the drug
should be administered.
• However, because of the scarcity of in vivo data and complexity of the underlying processes,
computational models that are capable of prediction Vd based solely on computed descriptors are
still under development.
28
29. 2.PLASMA PROTEIN BINDING (PBP)
• Drugs binding to a variety of plasma proteins such as serum albumin, as unbound drug primarily
contributes to pharmacological efficacy.
• The effect of PPB is an important consideration when evaluating the effective (unbound) drug
plasma concentration.
• The models proposed to predict PBB should not rely on the binding data of only one protein when
predicting plasma protein binding because it is a composite parameter reflecting interactions with
multiple protein.
29
30. 3.BLOOD-BRAIN BARRIER (BBB)
• The BBB maintains the restricted extracellular environment in the central nerve system.
• The evaluation of drug penetration through the BBB is an integral part of drug discovery and
development process.
• Again, because of the few experimental data derived from inconsistent protocols, most BBB permeation
prediction models are of limited practical use despite intensive efforts.
• Most approaches model log blood/brain (logBB), which is a measurement of the drug partitioning
between blood and brain tissue.
• The measurement is an indirect implication of BBB permeability, which does not discriminate between
free and plasma protein-bound solute.
30
32. DRUG EXCRETION
• The excretion or clearance of a drug is quantified by plasma clearance, which is defined as plasma volume
that has been cleared completely free of drug per unit of time.
• Together with Vd, it can assist in the calculation of drug half-life, thus determining the dosage regimen.
• Hepatic and renal clearances are the two main components of plasma clearance.
• No model has been reported that is capable of predicting plasma clearance solely from computed drug
structures.
• Current modeling efforts are mainly focused on estimating in vivo clearance from in vitro data.
• Just like other pharmacokinetic aspects, the hepatic and renal clearance process is also complicated by
presence of active transporters.
32
34. ACTIVE TRANSPORTERS
• Transporters should be an integral part of any ADMET modeling program because of their ubiquitous
presence on barrier membranes the substantial overlap between their substance many drugs.
• Unfortunately, because of our limited understanding of transporters, most prediction programs do not have
mechanism to incorporate the effect of active transport.
• However, interest in these transporters has resulted in a relatively large amount of in vitro data, which in
turn have enabled the generation of pharmacophore and QSAR models for many of them.
34
35. • These models have assisted in the understanding of the complex effects of transporters on drug
disposition, including absorption, distribution and excretion.
• Their incorporation into current modeling programs would also result in more accurate prediction of drug
disposition behavior.
35
36. P-GLYCOPROTEIN TRANSPORTER (P-gp)
• P- glycoprotein is an ATP dependent efflux transporter that transports a broad range of substrates out of the
cell.
• It affects drug disposition by reducing absorption and enhancing renal and hepatic excretion.
• For example , P-gp is known to limit the intestinal absorption of the anticancer drug paclitaxel and restricts
the CNS penetration of HIV protease inhibitors.
• It is also responsible for multiple drug resistance in cancer chemotherapy.
• Because of its significance in drug disposition and effective cancer Treatment,
• P-gp attracted numerous efforts and has become the most extensively studied transporter, with abundant
experimental data.
36
37. • Ekins and colleagues generated five computational pharmacophore models to predict the inhibition of P-gp
from in vitro on a diverse set of inhibitors with several cell system , including inhibition of digoxin
transport and verapamil binding in Caco-2 cells; vinblastine and calcein accumulation in P-gp-expressing
LLC-PK1 (L-MDR1) cells; and vinblastine binding in vesicles derived from CEM/VLB100 cells
• By comparing and merging all P-gp pharmacophore models, common areas of identical chemical features
such as hydrophobes , hydrogen bond acceptor , and ring aromatic features as well as their geometric
arrangement were identified to be the substrate requirement for P-gp.
37
38. • Similar transport requirements were reiterated in other works .
• More recently Cianchetta and colleagues combined alignment-independent 3D descriptors and
physicochemical descriptors to model inhibition of calcein accumulation in Caco-2 cells .
• Using a diverse set of 129 compounds, the authors derived a robust QSAR model that revealed two
hydrophobic features, two hydrogen bond acceptors, and the molecular dimension to be essential
determinants of P-gp-mediated transport.
• These identified transport requirements not only to help screen compounds with potential reflux related
bioavailability problems, but also to assist the identification of P-gp inhibitors.
38
39. • which when coadministered with target drugs would optimize their pharmacokinetic profile by increasing
bioavailability.
• In fact, a recent pharmacophore-based database screening has proposed 28 novel P-gp inhibitors from the
Derwent World Drug Index .
• Our own Catalyst pharmacophore searches of databases have also guided the identifi - cation of several
currently prescribed drugs that are P-gp inhibitors (μM), which was previously unknown
39
40. •inhibition of P-gp
• The inhibition of efflux pump is mainly done in order to improve the delivery of therapeutic agents. In
general, P-gp can be inhibited by three mechanisms: (i) blocking drug binding site either competitively,
non-competitively or allosterically;
• (ii) interfering with ATP hydrolysis; and (iii) altering integrity of cell membrane lipids.1,10,17–19
• The goal is to achieve improved drug bioavailability, uptake of drug in the targeted organ, and more
efficacious cancer chemotherapy through the ability to selectively block the action of P-gp. Inhibitors are
as structurally diverse as substrates.19 Many inhibitors (verapamil, cyclosporin A, transflupenthixol, etc.)
are themselves transported by P-gp.
40
41. BREAST CANCER RESISTANCE PROTEIN (BCRP)
• Breast cancer resistance protein is another ATP dependent efflux transporter that confers resistance to a
variety of anticancer agents anthracyclines.
• In addition to high level of expression in hematological malignancies and solid tumors, BCRP is also
expressed in intestine, liver and brain thus implicating its very complicated role in drug disposition behavior.
• Zhang and colleagues generated a BCRP 3DQSAR model by analyzing structure and activity of 25
flavonoid analogs
41
42. • The model anaphasizes very specific structural feature requirements for BCRP such as the presence of a
2,3-double bond in ring C and hydroxylation at position 5.
• Because the model in only based on a set of closely related structure instead of diverse set, it should be
applied with caution.
• Satisfying the transport model would render a compound susceptible to BCRP, but not fitting into the
model does not necessarily exclude the candidate from BCRP transport.
• In fact, this caveat should be considered for all predictive in silico models, because no model can cover all
possible chemical space.
42
43. Figure 20.2 Pharmacophore models for P-gp inhibition. A. P-gp inhibition pharmacophore
aligned with the potent inhibitor LY335979. B. P-gp substrate pharmacophore
aligned with verapamil. C. P-gp inhibition pharmacophore 2 aligned with
LY335979. Green indicates H-bond acceptor feature, and cyan indicates hydrophobic
feature. See color plate.
43
44. NUCLEOSIDE TRANSPORTER
• Nucleoside transporters transport both naturally occurring nucleoside and synthetic nucleoside analogs
that are used as anticancer drugs anti viral drugs.
• There are various types of nucleoside transporter, including concentrative nucleoside transporter (CNT1
CNT2 CNT3) and equilibrative nucleoside transporter(ENT1 ENT2 ENT3) each have different substrate
specificity.
• ENT have broad affinity, low selectivity and are ubiquitously located.
• CNT have high affinity, selective located in epithelia of intestine kidney, liver and brain, indicating their
involvement in drug disposition, distribution and excretion.
44
45. • The first 3D-QSAR model for nucleoside transporter was generated back in 1190.
• It is an oversimplified general model limited by the scarce experimental data at that time. A
• more comprehensive study generated distinctive models for CNT1, CNT2, and ENT1 with both
pharmacophore and 3DQSAR modeling techniques
• All models show the common features required for nucleoside transporter mediated transport: two
hydrophobic features and one hydrogen bond acceptor on the pentose ring.
45
46. • The individual models also reveal the subtle characteristic requirements for each specific transporter.
• The modeling results also support the previous observation that CNT2 is the most selective transporter
whereas ENT1 has the broadest inhibitor specificity.
• More recently, we performed the same analyses and generated pharmacophore and 3D-QSAR models for
CNT3 by assessing the transport activity of 33 nucleoside analogs .
• These studies represent a comprehensive evaluation of transport requirements of all three types of CNTs.
46
47. • Human peptide transporter is a low affinity high capacity to peptide transport system that transport a diverse
range of substrate including B-lactam antibiotics and ACE inhibitors.
• It is mainly expressed in intestine and kidney affecting drug absorption and excretion.
• A pharmacophore model is based on three high affinity substrates(gly-sar, bestatin, enalapril) were taken
• They recognize two hydrophobic features, one hydrogen bond donor, one hydrogen bond acceptor, and
negative ionizable feature to be hPEPT1 transport requirements.
47
HUMAN PEPTIDE TRANSPORTER (hPEPT1)
48. • The pharmacophore model was subsequently applied to screen the CMC database with over 8000 drug
like molecules.
• The anti Diabetic repaglinide and HMG- CoA reductase inhibitor Fluvastatin were suggested by the
model and later verified to inhibit Hpept1 with submillimolar potency.
• This work demonstrated the potential of applying in silico models in high throughput database
screening.
48
49. HUMAN APICAL SODIUM-DEPENDENT BILE ACID
TRANSPORTER (ASBT)
• The human apical sodium- dependent bile acid transporter is high efficacy, high capacity transporter
expressed on the apical membrane of intestinal epithelial cells and cholangiocytes.
• It assist absorption of bile acid and their analogs, thus providing a additional intestinal target for improving
drug absorption.
• Baringhaus and colleagues developed a pharmacophore model based on a training set of 17 chemically
diverse inhibitors of ASBT.
49
50. • The model revealed ASBT transport requirements as one hydrogen bond donor, one hydrogen bond
acceptor, one negative charge, and three hydrophobic centres.
• These 3D- QSAR model derived from the structure and activity of 30 ASBT inhibitors and substrate.
50
51. ORGANIC CATIONIC TRANSPORTER (OCT)
• The organic cation transporter facilitate the uptake of many cationic drugs across different membranes of
kidney and intestine epithelia.
• A broad range of drugs or their metabolites fall into chemical class of organic cation including
antiarrythmics , B-adrenoaceptor blocking agents, Antihistaminics , antiviral agents , and skeletal muscle-
relaxing agents
• These OCTs have been cloned from different species, OCT1/2/3.
• A human OCT pharmacophore model was developed by analyzing the extent of inhibition of TEA uptake in
HeLa cells of 22 diverse molecules.
51
52. • The model suggests the transport requirements of human OCT1 as three hydrophobic features and one
positive ionizable feature . Molecular determinants of substrate binding to human OCT2 and rabbit OCT2
were recently reported
• Both 2D and 3D-QSAR analyses were performed to identify and discriminate the binding requirements of
two orthology.
• The models showed the same chemical features, highlighting their similarities. However, the orientation of a
critical hydrogen bonding feature set the two orthologs apart.
• This work illustrates the sensitivity of in silico modeling in discriminating similar transporters.
52
53. ORGANIC ANION TRANSPORTING
POLYPEPTIDE (OATP)
• Organic anion transporting polypeptides influence the plasma conc. of many drugs by actively transporting
them across various tissue membranes such as liver, intestine, lung and brain.
• Because of their broad substrate specificity,OATP transport not only organic anionic drugs but also organic
cationic drugs.
• human OATPs have been identified, and the substrate binding requirements of the best-studied OATP1B1
were successfully modeled with metapharmacophre approach recently.
• Through assessing a training set of 18 diverse molecules, the metapharmacophore model identifies three
hydrophobic features flanked by two hydrogen bond acceptor features to be essential requirement for
OATP1B1 transport.
53
55. BBB-CHOLINE TRANSPORTER
• The BBB-choline transporter is a native nutrient transporter that transports choline, a charged cation, across
the BBB into the CNS.
• Its active transport assists the BBB penetration of choline like compounds, and understanding its structural
requirements should afford a more accurate prediction of BBB permeation. Even though the BBB-choline
transporter has not been cloned,
• Geldenhuys and colleagues applied a combination of empirical and theoretical methodologies to study its
binding requirements.
55
56. • The 3D-QSAr models were built with emperical ki data obtained from in situ rat brain perfusion experiments
with structurally diverse set of compounds were identified to be important for BBB-choline transporter
recognition.
• Even though the model statistical significance is not optimal (q2 < 0.5), it does provide a useful estimation of
BBB-choline transporter binding requirements.
• More accurate in silico models could be generated once higher-quality data from the cloned BBB-choline
transporter are available.
56